bconv | Python library for converting between BioNLP formats | Natural Language Processing library

by lfurrer Python Version: 1.2.1 License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | bconv Summary

bconv is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. bconv has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However bconv build file is not available. You can install using 'pip install bconv' or download it from GitHub, PyPI.

bconv offers format conversion and manipulation of documents with text and annotations. It supports various popular formats used in natural-language processing for biomedical texts.

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Quality

Security

License

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Support

bconv has a low active ecosystem.

It has 8 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 3 have been closed. On average issues are closed in 6 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of bconv is 1.2.1

Quality

bconv has 0 bugs and 0 code smells.

Security

bconv has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

bconv code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

bconv is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

bconv releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

bconv has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

It has 4943 lines of code, 364 functions and 34 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed bconv and discovered the below as its top functions. This is intended to give you an instant insight into bconv implemented functionality, and help decide if they suit your requirements.

Return a list of codepoint indices
Iterate over byte indices
Iterator over byte indices in a string
Yield the index of codepoints in text
Parse a Document node
Adjust entity spans
Add a section
Return text node matching xpath
Write entities to stream
Update the text with the given text
Create a Document from a source file
Split text into sentences
Write the given document to a tar file
Iterate over text
Create a collection from multiple documents
Select fields for annotation
Yield documents from a source file
Return a Document object
Add entities to the annotations
Iterate over entities
Write content to stream
Add entities to the corpus
Return an iterator over all entities in this sentence
Yield row ids from tokens
Dump content to dest
Iterate through all documents in source

Get all kandi verified functions for this library.

bconv Key Features

No Key Features are available at this moment for bconv.

bconv Examples and Code Snippets

bconv,Usage

Python

Lines of Code : 16

License : Permissive (MIT)

Copy

>>> import bconv
>>> coll = bconv.load('path/to/example.xml', fmt='bioc_xml')
>>> coll


>>> coll[0]


>>> sent = coll[0][3][5]
>>> sent.text
'A Live cell imaging reveals that expression of GFP‐KS

bconv,Installation

Python

Lines of Code : 1

License : Permissive (MIT)

Copy

$ pip install bconv

Community Discussions

Trending Discussions on bconv

RuntimeError: shape '[10, 3, 150, 150]' is invalid for input of size 472500

QUESTION

RuntimeError: shape '[10, 3, 150, 150]' is invalid for input of size 472500

Asked 2021-Nov-01 at 11:04

I'm trying to perform a Convolutional operation on the covid CT Dataset and constantly getting this error. My image size in the train loader was (10, 150, 150, 3) and I reshaped it into [10, 3, 150, 150], using torch.reshape(). Can anybody help me with problem

My CNN Code

...

ANSWER

Answered 2021-Nov-01 at 11:04

Here I'm considering your whole model including the third block consisting of conv3, bn3, and relu3. There are a few things to note:

Reshaping is substantially different from permuting the axes. When you say you have an input shape of (batch_size, 150, 150, 3), it means the channel axis is last. Since PyTorch 2D builtin layers work in the NHW format you need to permute the axes: you can do so with torch.Tensor.permute:

Source https://stackoverflow.com/questions/69794901

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